Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x245ed2a0710>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x245ed353320>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.2.1
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32,  name='learning_rate')

    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
['File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\runpy.py", line 184, in _run_module_as_main\n    "__main__", mod_spec)', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\runpy.py", line 85, in _run_code\n    exec(code, run_globals)', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\ipykernel\\__main__.py", line 3, in <module>\n    app.launch_new_instance()', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\traitlets\\config\\application.py", line 658, in launch_instance\n    app.start()', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\ipykernel\\kernelapp.py", line 477, in start\n    ioloop.IOLoop.instance().start()', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\zmq\\eventloop\\ioloop.py", line 177, in start\n    super(ZMQIOLoop, self).start()', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\tornado\\ioloop.py", line 888, in start\n    handler_func(fd_obj, events)', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\tornado\\stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py", line 440, in _handle_events\n    self._handle_recv()', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py", line 472, in _handle_recv\n    self._run_callback(callback, msg)', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py", line 414, in _run_callback\n    callback(*args, **kwargs)', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\tornado\\stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\ipykernel\\kernelbase.py", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\ipykernel\\kernelbase.py", line 235, in dispatch_shell\n    handler(stream, idents, msg)', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\ipykernel\\kernelbase.py", line 399, in execute_request\n    user_expressions, allow_stdin)', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\ipykernel\\ipkernel.py", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\ipykernel\\zmqshell.py", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\IPython\\core\\interactiveshell.py", line 2698, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\IPython\\core\\interactiveshell.py", line 2808, in run_ast_nodes\n    if self.run_code(code, result):', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\IPython\\core\\interactiveshell.py", line 2862, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File "<ipython-input-5-3d66d422fbee>", line 22, in <module>\n    tests.test_model_inputs(model_inputs)', 'File "D:\\DataScience\\DLUdacity\\deep-learning\\face_generation\\problem_unittests.py", line 12, in func_wrapper\n    result = func(*args)', 'File "D:\\DataScience\\DLUdacity\\deep-learning\\face_generation\\problem_unittests.py", line 68, in test_model_inputs\n    _check_input(learn_rate, [], \'Learning Rate\')', 'File "D:\\DataScience\\DLUdacity\\deep-learning\\face_generation\\problem_unittests.py", line 34, in _check_input\n    _assert_tensor_shape(tensor, shape, \'Real Input\')', 'File "D:\\DataScience\\DLUdacity\\deep-learning\\face_generation\\problem_unittests.py", line 20, in _assert_tensor_shape\n    assert tf.assert_rank(tensor, len(shape), message=\'{} has wrong rank\'.format(display_name))', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\tensorflow\\python\\ops\\check_ops.py", line 617, in assert_rank\n    dynamic_condition, data, summarize)', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\tensorflow\\python\\ops\\check_ops.py", line 571, in _assert_rank_condition\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\tensorflow\\python\\util\\tf_should_use.py", line 170, in wrapped\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\tensorflow\\python\\util\\tf_should_use.py", line 139, in _add_should_use_warning\n    wrapped = TFShouldUseWarningWrapper(x)', 'File "C:\\ProgramData\\Anaconda3\\envs\\carnd-term1-gpu\\lib\\site-packages\\tensorflow\\python\\util\\tf_should_use.py", line 96, in __init__\n    stack = [s.strip() for s in traceback.format_stack()]']
==================================
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
#layer helper functions
def leaky_relu(x, alpha=0.05, name='leaky_relu'): 
    return tf.maximum(x, alpha * x, name=name)
    
def conv_layer(input,depth,kernel_size,strides):
    return tf.layers.conv2d(input, depth, kernel_size, strides=strides, padding='same',
                                kernel_initializer=tf.contrib.layers.xavier_initializer())

def conv_transpose_layer(input,depth,kernel_size,strides,padding='same'):
    return tf.layers.conv2d_transpose(input, depth, kernel_size, strides=strides, padding=padding,
                                kernel_initializer=tf.contrib.layers.xavier_initializer())
In [7]:
def discriminator(images, reuse=False, alpha=0.2,keep_prob = 0.5):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """

    rate = 1 - keep_prob
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 28x28x3
        
        x1 = conv_layer(images,128,5,2)
        relu1 = leaky_relu(x1)
        #relu1 = tf.layers.dropout(x1, rate)
        # 14x14x128
        
        x2 = conv_layer(relu1,256,5,2)
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = leaky_relu(bn2)
        #relu2 = tf.layers.dropout(relu2, rate)
        # 7x7x256
        
        x3 = conv_layer(relu2,512,5,2)
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = leaky_relu(bn3)
        #relu3 = tf.layers.dropout(relu3, rate)
        # 4x4x512
        
    
        
        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits

 



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [11]:
def generator(z, out_channel_dim, is_train=True, alpha=0.2):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
  
    reuse = not is_train
    training = is_train
    with tf.variable_scope('generator', reuse=reuse):
        x0 = tf.layers.dense(z, 2*2*1024)        
        x0 = tf.reshape(x0, (-1, 2, 2, 1024))
        x0 = tf.layers.batch_normalization(x0, training=is_train)
        x0 = leaky_relu( x0)
        # 2x2x1024 now

        x1 = tf.layers.conv2d_transpose(x0, 512, 5, strides=2, padding='same')
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = leaky_relu( x1)
        # 4x4x512 now

        x2 = tf.layers.conv2d_transpose(x1, 256, 4, strides=1, padding='valid')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = leaky_relu( x2)
        # 7x7x256

        x21 = tf.layers.conv2d_transpose(x2, 256, 1, strides=1, padding='same')
        x21 = tf.layers.batch_normalization(x2, training=is_train)
        x21 = leaky_relu( x21)
        # 7x7x256


        x3 = tf.layers.conv2d_transpose(x21, 128, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = leaky_relu( x3)
        # 14x14x256 now

        x4 = tf.layers.conv2d_transpose(x3, 128, 1, strides=1, padding='same')
        x4 = tf.layers.batch_normalization(x3, training=is_train)
        x4 = leaky_relu(x4)
        # 14x14x256 now


        # Output layer
        logits = tf.layers.conv2d_transpose(x4, out_channel_dim, 5, strides=2, padding='same')
        # 28x28x3 now

        out = tf.tanh(logits)

        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed
In [ ]:
 

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [12]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    alpha = 0.2
    g_model = generator(input_z, out_channel_dim, alpha=alpha)
    d_model_real, d_logits_real = discriminator(input_real, alpha=alpha)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha)
    smooth = 0.1
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*(1.0 - smooth)))
    #d_loss_real = tf.reduce_mean(
    #    tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [13]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
     # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [14]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [15]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    show_every = 100
    steps = 0
    n_images = 25
    print_every=100
    losses = [] 
    _, img_width, img_height, img_channels = data_shape
    real_input, input_z, lr = model_inputs(img_width, img_height, img_channels, z_dim)
    
    d_loss, g_loss = model_loss(real_input, input_z, img_channels)
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1                
                batch_images *= 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                
                _ = sess.run(d_train_opt, feed_dict={
                        real_input: batch_images, 
                        input_z: batch_z, 
                        lr: learning_rate})
                _ = sess.run(g_train_opt, feed_dict={
                        real_input: batch_images, 
                        input_z: batch_z,
                        lr: learning_rate})               

                if steps % print_every == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, real_input: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})
                    
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    losses.append((train_loss_d, train_loss_g))

                if steps % show_every == 0:
                    show_generator_output(sess, n_images, input_z, img_channels, data_image_mode)
                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [ ]:
batch_size = 32
z_dim = 100
learning_rate = 5e-4
beta1 = 0.4


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.8648... Generator Loss: 1.5534
Epoch 1/2... Discriminator Loss: 1.0513... Generator Loss: 2.4849
Epoch 1/2... Discriminator Loss: 1.4257... Generator Loss: 0.5497
Epoch 1/2... Discriminator Loss: 1.0328... Generator Loss: 1.4535
Epoch 1/2... Discriminator Loss: 1.0536... Generator Loss: 1.1311
Epoch 1/2... Discriminator Loss: 0.9163... Generator Loss: 1.0791
Epoch 1/2... Discriminator Loss: 0.7288... Generator Loss: 1.7277
Epoch 1/2... Discriminator Loss: 0.8534... Generator Loss: 1.2802
Epoch 1/2... Discriminator Loss: 0.8341... Generator Loss: 1.4651
Epoch 1/2... Discriminator Loss: 0.6660... Generator Loss: 1.9561
Epoch 1/2... Discriminator Loss: 1.0208... Generator Loss: 2.1403
Epoch 1/2... Discriminator Loss: 0.9292... Generator Loss: 1.1857
Epoch 1/2... Discriminator Loss: 1.3522... Generator Loss: 0.6051
Epoch 1/2... Discriminator Loss: 0.9634... Generator Loss: 1.1919
Epoch 1/2... Discriminator Loss: 0.9804... Generator Loss: 1.2599
Epoch 1/2... Discriminator Loss: 1.2107... Generator Loss: 0.6834
Epoch 1/2... Discriminator Loss: 0.6985... Generator Loss: 1.6408
Epoch 1/2... Discriminator Loss: 1.0875... Generator Loss: 1.9460
Epoch 2/2... Discriminator Loss: 0.9458... Generator Loss: 1.0617
Epoch 2/2... Discriminator Loss: 1.1670... Generator Loss: 2.3470
Epoch 2/2... Discriminator Loss: 0.9215... Generator Loss: 1.9792
Epoch 2/2... Discriminator Loss: 0.9781... Generator Loss: 1.1261
Epoch 2/2... Discriminator Loss: 0.9554... Generator Loss: 1.5012
Epoch 2/2... Discriminator Loss: 1.2962... Generator Loss: 0.5731
Epoch 2/2... Discriminator Loss: 1.0908... Generator Loss: 1.0248
Epoch 2/2... Discriminator Loss: 0.9379... Generator Loss: 0.9994
Epoch 2/2... Discriminator Loss: 1.2902... Generator Loss: 0.5892
Epoch 2/2... Discriminator Loss: 1.1321... Generator Loss: 0.8287
Epoch 2/2... Discriminator Loss: 1.1013... Generator Loss: 1.5927
Epoch 2/2... Discriminator Loss: 0.9350... Generator Loss: 2.0443
Epoch 2/2... Discriminator Loss: 1.1234... Generator Loss: 0.7627
Epoch 2/2... Discriminator Loss: 0.7103... Generator Loss: 1.4989
Epoch 2/2... Discriminator Loss: 1.3043... Generator Loss: 0.6802
Epoch 2/2... Discriminator Loss: 0.9481... Generator Loss: 1.6715
Epoch 2/2... Discriminator Loss: 1.4360... Generator Loss: 0.5271
Epoch 2/2... Discriminator Loss: 1.1934... Generator Loss: 0.9756
Epoch 2/2... Discriminator Loss: 0.9859... Generator Loss: 1.7077

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [18]:
batch_size = 32
z_dim = 100
learning_rate = 5e-4
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 0.5142... Generator Loss: 2.4438
Epoch 1/1... Discriminator Loss: 1.2134... Generator Loss: 0.7724
Epoch 1/1... Discriminator Loss: 1.1466... Generator Loss: 0.9817
Epoch 1/1... Discriminator Loss: 0.9573... Generator Loss: 1.8864
Epoch 1/1... Discriminator Loss: 0.8456... Generator Loss: 1.4165
Epoch 1/1... Discriminator Loss: 1.0435... Generator Loss: 1.0195
Epoch 1/1... Discriminator Loss: 0.8951... Generator Loss: 1.6172
Epoch 1/1... Discriminator Loss: 0.7725... Generator Loss: 1.5879
Epoch 1/1... Discriminator Loss: 0.8408... Generator Loss: 1.5195
Epoch 1/1... Discriminator Loss: 1.1167... Generator Loss: 1.0854
Epoch 1/1... Discriminator Loss: 1.0595... Generator Loss: 0.9135
Epoch 1/1... Discriminator Loss: 0.8338... Generator Loss: 1.3988
Epoch 1/1... Discriminator Loss: 0.8372... Generator Loss: 1.2465
Epoch 1/1... Discriminator Loss: 1.0373... Generator Loss: 1.7832
Epoch 1/1... Discriminator Loss: 0.8215... Generator Loss: 2.3614
Epoch 1/1... Discriminator Loss: 1.0629... Generator Loss: 0.8210
Epoch 1/1... Discriminator Loss: 0.9234... Generator Loss: 1.5724
Epoch 1/1... Discriminator Loss: 1.3605... Generator Loss: 3.9007
Epoch 1/1... Discriminator Loss: 0.9161... Generator Loss: 1.1861
Epoch 1/1... Discriminator Loss: 0.7183... Generator Loss: 1.4374
Epoch 1/1... Discriminator Loss: 1.1299... Generator Loss: 0.7242
Epoch 1/1... Discriminator Loss: 1.1212... Generator Loss: 0.7426
Epoch 1/1... Discriminator Loss: 0.7843... Generator Loss: 1.7360
Epoch 1/1... Discriminator Loss: 0.6250... Generator Loss: 1.9432
Epoch 1/1... Discriminator Loss: 1.0584... Generator Loss: 0.9257
Epoch 1/1... Discriminator Loss: 1.0816... Generator Loss: 2.2463
Epoch 1/1... Discriminator Loss: 0.8436... Generator Loss: 1.7033
Epoch 1/1... Discriminator Loss: 0.8643... Generator Loss: 1.3263
Epoch 1/1... Discriminator Loss: 0.6964... Generator Loss: 1.6570
Epoch 1/1... Discriminator Loss: 0.7039... Generator Loss: 1.6204
Epoch 1/1... Discriminator Loss: 1.1279... Generator Loss: 0.7422
Epoch 1/1... Discriminator Loss: 0.7021... Generator Loss: 1.5273
Epoch 1/1... Discriminator Loss: 0.7685... Generator Loss: 1.2970
Epoch 1/1... Discriminator Loss: 0.5814... Generator Loss: 1.9787
Epoch 1/1... Discriminator Loss: 0.8403... Generator Loss: 2.1070
Epoch 1/1... Discriminator Loss: 0.7728... Generator Loss: 1.7643
Epoch 1/1... Discriminator Loss: 0.8444... Generator Loss: 1.7988
Epoch 1/1... Discriminator Loss: 0.7360... Generator Loss: 1.5735
Epoch 1/1... Discriminator Loss: 0.8383... Generator Loss: 1.2228
Epoch 1/1... Discriminator Loss: 0.8054... Generator Loss: 1.3105
Epoch 1/1... Discriminator Loss: 0.9329... Generator Loss: 1.1900
Epoch 1/1... Discriminator Loss: 0.8209... Generator Loss: 1.6264
Epoch 1/1... Discriminator Loss: 1.1966... Generator Loss: 0.6874
Epoch 1/1... Discriminator Loss: 0.8655... Generator Loss: 1.1972
Epoch 1/1... Discriminator Loss: 0.9809... Generator Loss: 0.9865
Epoch 1/1... Discriminator Loss: 0.7170... Generator Loss: 1.4544
Epoch 1/1... Discriminator Loss: 0.7841... Generator Loss: 1.7434
Epoch 1/1... Discriminator Loss: 0.7795... Generator Loss: 1.4878
Epoch 1/1... Discriminator Loss: 1.0431... Generator Loss: 0.9083
Epoch 1/1... Discriminator Loss: 0.8001... Generator Loss: 1.8206
Epoch 1/1... Discriminator Loss: 0.7175... Generator Loss: 1.6959
Epoch 1/1... Discriminator Loss: 0.9163... Generator Loss: 1.1514
Epoch 1/1... Discriminator Loss: 0.8340... Generator Loss: 1.2894
Epoch 1/1... Discriminator Loss: 0.7392... Generator Loss: 1.7665
Epoch 1/1... Discriminator Loss: 1.1131... Generator Loss: 2.4594
Epoch 1/1... Discriminator Loss: 0.7360... Generator Loss: 1.4664
Epoch 1/1... Discriminator Loss: 0.7401... Generator Loss: 1.8341
Epoch 1/1... Discriminator Loss: 0.7841... Generator Loss: 1.3099
Epoch 1/1... Discriminator Loss: 0.8541... Generator Loss: 1.8577
Epoch 1/1... Discriminator Loss: 0.8900... Generator Loss: 1.4138
Epoch 1/1... Discriminator Loss: 1.1056... Generator Loss: 0.7696
Epoch 1/1... Discriminator Loss: 1.0210... Generator Loss: 0.8879
Epoch 1/1... Discriminator Loss: 0.8920... Generator Loss: 2.0037

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.